import tensorflow as tf
from tensorflow import keras

import numpy as np

imdb = keras.datasets.imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

print(type(train_data))
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
print(train_data[0])

# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()

# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()} 
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

print(decode_review(train_data[0]))

train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                        value=word_index["<PAD>"],
                                                        padding='post',
                                                        maxlen=256)

test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                       value=word_index["<PAD>"],
                                                       padding='post',
                                                       maxlen=256)

vocab_size = 10000

model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

model.summary()

# what is the default learning_rate
model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='binary_crossentropy',
              metrics=['accuracy'])

x_val = train_data[:10000]
partial_x_train = train_data[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
print(partial_x_train[0])

history = model.fit(partial_x_train,
          partial_y_train,
          epochs=40,
          batch_size=512,
          validation_data=(x_val, y_val),
          verbose=1)

results = model.evaluate(test_data, test_labels)

print(results)
history_dict = history.history


#import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)
#dict_keys(['loss', 'acc', 'val_acc', 'val_loss'])

#import matplotlib.pyplot as plt
#
## "bo" is for "blue dot"
#plt.plot(epochs, loss, 'bo', label='Training loss')
## b is for "solid blue line"
#plt.plot(epochs, val_loss, 'b', label='Validation loss')
#plt.title('Training and validation loss')
#plt.xlabel('Epochs')
#plt.ylabel('Loss')
#plt.legend()
#
#plt.show()
#
